AIMC Topic: Meat

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Rapid analysis of chinese blanched chicken (Mahuang, Tuer, and Huangyou) based on volatile compounds and machine learning.

Food chemistry
To promote the intelligent development of the poultry industry, this study systematically analyzed volatile organic compounds (VOCs) in three Chinese Blanched Chicken (CBC) breeds (Mahuang, Tuer, and Huangyou) using gas chromatography-ion mobility sp...

Decoding the spectrum of meat quality: advances in hyperspectral imaging for multi-attribute analysis.

Food chemistry
Hyperspectral imaging (HSI) has emerged as a powerful non-destructive technique for evaluating fresh meat quality across multiple attributes simultaneously. This review critically examines recent advances in HSI applications for fresh beef, pork, and...

Colorimetric sensor array enabled by deep eutectic solvent-regulated anthocyanins and electrospun polylactic acid nanofiber for pork freshness monitoring.

Food chemistry
In this study, we constructed a bilayer colorimetric sensor array with carboxymethyl cellulose, deep eutectic solvent-regulated anthocyanins, and electrospun polylactic acid nanofibers for real-time monitoring pork freshness. The nanofibers were laye...

A multi-task deep learning model based on transformer for simultaneously evaluating the TVB-N and TVC contents of chicken breasts using two different hyperspectral imaging.

Food chemistry
Accurate assessment of freshness is crucial for ensuring quality and safety in the chicken meat industry. This study developed a Multi-task Interleaved Group Transformer Model (MIGTM) integrating dual hyperspectral imaging (HSI) data to simultaneousl...

Leveraging pre-trained computer vision models for accurate classification of meat freshness.

Food chemistry
Increasing concerns about food quality and safety have led to research into ways to assess meat freshness. Advances in deep learning, particularly image classification, enable up new possibilities for fast and non-destructive methods of evaluating me...

Nondestructive freshness recognition of chicken breast meat based on deep learning.

Scientific reports
Identifying chicken breast freshness is an important component of poultry food safety. Traditional methods for chicken breast freshness recognition suffer from issues such as high cost, difficulty in recognition, and low efficiency. In this study, th...

Machine learning for polycyclic aromatic hydrocarbons analysis in roasted lamb: new insights from spectral and chemical data.

Food chemistry
Polycyclic aromatic hydrocarbons (PAHs) generated during lamb roasting pose health risks but are difficult to predict due to their low concentrations and complex features. Existing models fail to address data scarcity and low-concentration prediction...

Artificial intelligence-driven food quality prediction: Applying machine learning ensemble models for dynamic forecasting of pork pH and meat color changes.

Food chemistry
This study presents a food chemistry-driven approach to predict post-slaughter pork quality dynamics, focusing on the biochemical mechanisms governing pH evolution and meat color development over 48 h. The interconversion of myoglobin redox states an...

Linking lipidomics to meat quality: A review on texture and flavor in livestock and poultry.

Food chemistry
This review explores the application of lipidomics in livestock and poultry meat research, focusing on lipid identification and analysis from the perspectives of meat quality and flavor. We offer a comprehensive overview of lipid extraction methods a...

Assessment of POPs in foods from western China: Machine learning insights into risk and contamination drivers.

Environment international
Persistent organic pollutants (POPs), including PCDD/Fs, PCBs, and PBDEs, are major environmental and food safety concerns due to their bioaccumulative and toxic properties. However, comprehensive research on the concentrations and influencing factor...